Localization with Diverse Dataset for Autonomous Vehicles

2. The computer-implemented method of claim 1, wherein the determined current pose value comprises the predetermined pose value of the one or more pre-localized sensor observations determined to be the closest match to the current sensor reading representation.

3. The computer-implemented method of claim 1, wherein each pre-localized sensor observation and the current sensor reading representation comprises a vector of features determined from a respective sensor reading.

4. The computer-implemented method of claim 1, wherein the one or more sensors comprises at least one of one or more LIDAR sensors or one or more cameras.

6. The computer-implemented method of claim 1, wherein the machine-learned retrieval model is trained with respect to a triplet loss function determined for each pre-localized sensor observation in the ground truth dataset, the triplet loss function defined in terms of a positive input, an anchor input, and a negative input, wherein a first threshold for comparing the positive input to the anchor input is less than a second threshold for comparing the negative input to the anchor input.

7. The computer-implemented method of claim 6, wherein each of the positive input, the negative input, and the anchor input have an associated heading angle, and wherein the heading angles for each of the positive input, the negative input and the anchor input are within a predetermined angular range.

8. The computer-implemented method of claim 6, wherein the positive input and the negative input are captured along at least one different trip than a trip along which the anchor input is captured.

9. The computer-implemented method of claim 1, wherein the determined current pose value has an accuracy within about 10 centimeters.

10. The computer-implemented method of claim 1, wherein the ground truth dataset comprises pre-localized sensor observations taken under differing conditions of at least one of weather, season, illumination, construction, occlusion, or dynamic objects.

11. The computer-implemented method of claim 10, wherein the differing conditions comprise at least one of LIDAR occlusion, image occlusion, temperature, cloud cover, precipitation intensity, sun angle over horizon, visibility, UV conditions, precipitation type, or trip.

12. The computer-implemented method of claim 1, wherein the pre-localized sensor observations are localized by vehicle dynamics and LIDAR registration against a dense scan of a region.

13. The computer-implemented method of claim 1, wherein the ground truth dataset is annotated with granular labels from at least one of historical weather data, historical astronomical data, or degree of occlusion.

14. The computer-implemented method of claim 13, further comprising filtering, from the plurality of candidate features, one or more of the plurality of ground truth features based at least in part on the granular labels.

16. The computer-implemented method of claim 15, wherein the dense scan comprises a LIDAR scan.

17. The computer-implemented method of claim 15, wherein the plurality of dataset sensor observations are captured under differing conditions, and wherein the differing conditions comprise at least one of LIDAR occlusion, image occlusion, temperature, cloud cover, precipitation intensity, sun angle over horizon, visibility, UV conditions, precipitation type, or trip.

19. The computer-implemented method of claim 15, wherein the ground truth dataset is annotated with granular labels from at least one of historical weather data, historical astronomical data, or degree of occlusion.